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Deconstructing major depression: a validation study of the DSM-IV symptomatic criteria

  • V. Lux (a1) and K. S. Kendler (a2)

Abstract

Background

The DSM-IV symptomatic criteria for major depression (MD) derive primarily from clinical experience with modest empirical support.

Method

The sample studied included 1015 (518 males, 497 females) Caucasian twins from a population-based registry who met criteria for MD in the year prior to the interview. Logistic regression analyses were conducted to compare the associations of: (1) single symptomatic criterion, (2) two groups of criteria reflecting cognitive and neurovegetative symptoms, with a wide range of potential validators including demographic factors, risk for future episodes, risk of MD in the co-twin, characteristics of the depressive episode, the pattern of co-morbidity and personality traits.

Results

The individual symptomatic criteria showed widely varying associations with the pattern of co-morbidity, personality traits, features of the depressive episode and demographic characteristics. When examined separately, these two criteria groups showed robust differences in their patterns of association, with the validators with the cognitive criteria generally producing stronger associations than the neurovegetative.

Conclusions

Among depressed individuals, individual DSM-IV symptomatic criteria differ substantially in their predictive relationship with a range of clinical validators. These results challenge the equivalence assumption for the symptomatic criteria for MD and suggest a more than expected degree of ‘covert’ heterogeneity among these criteria. Part of this heterogeneity is captured by the distinction between cognitive versus neurovegetative symptoms, with cognitive symptoms being more strongly associated with most clinically relevant characteristics. Detailed psychometric evaluation of DSM-IV criteria is overdue.

Copyright

Corresponding author

*Address for correspondence: Dr K. S. Kendler, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Box 980126, Richmond, VA 23298–0126, USA. (Email: kendler@vcu.edu)

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